AD agent-feishu-direct-tools-patch
One-time/on-demand patch for openclaw-lark (no webhook LarkTicket paths). Implements agent-session-context, session-key-feishu, syncTicketContextForToolClient, createToolClient third arg, hooks, calendar effectiveSenderOpenId, toolClient sweep. After file edits, tell the user what changed and that they MUST restart the Gateway. BEFORE starting file edits, Agent must brief the user and wait for explicit confirmation. On failure or Feishu broken after patch, user may reinstall with: npx -y @larksuite/openclaw-lark install. Prefer AI-assisted dev tools or temporary OpenClaw skill load—avoid keeping this skill permanently enabled in skills.entries. Use when porting or re-applying after merge conflicts.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5960 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 44/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 10 mutating operations with no state check
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70Execution cost. Instruction body is 5960 tokens
- 100Steps. 91 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 21 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 707: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 91 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.